Repa (REgular PArallel arrays)

Repa provides high performance, regular, multi-dimensional, shape polymorphic parallel arrays. All numeric data is stored unboxed. Functions written with the Repa combinators are automatically parallel provided you supply +RTS -Nwhatever on the command line when running the program. Repa means "turnip" in Russian. If you don't like turnips then this library probably isn't for you.

Download

Repa is split up into a few packages to help control dependencies.

​repa	The base library, defining the array type and combinators.
​repa-bytestring	Conversions to and from ByteString.
​repa-io	Reading and writing arrays in various formats, including BMP.
​repa-algorithms	Some reusable matrix algorithms.
​repa-devil	Bindings to the DevIL image library.
​repa-examples	Example applications.

If you want the lot then just install the examples, and this will pull down the rest.

cabal install repa-examples

From the source repo

The darcs source repos are at ​http://code.ouroborus.net/repa/

Frequently Asked Questions (FAQ)

Q: GHC complains: ghc.exe: could not execute opt what's up with that?
A: opt is the LLVM optimiser. You need to install ​LLVM.

Q: How to I map a function across all slices in an array?
A: This is not possible in general because we can't guarantee that the worker function will return a slice with the same shape as the original. If the slices only contain a few elements (up to six) then your best bet is to use an array of tuples and use the regular map function to apply the worker to all the tuples. This approach is limited to 6-tuples because that's the maximum the Data.Vector library currently supports, and Repa uses Data.Vector to store manifest arrays. See http://repa.ouroborus.net/ticket/22 for discussion.

Q: Where do I ask further questions?
A: Ask on ​Stack Overflow, then send a message to repa@ouroborus.net if that doesn't help.

Report a bug

To report bugs, request features, or get an account on the trac, please send email to repa@ouroborus.net

Papers and Tutorials

​Regular Shape Polymorphic Arrays in Haskell

• Describes the overall approach to fusion, and how the shape polymorphism works.
• Since this paper was published, the internals have changed slightly, but the overall structure is the same.

​Efficient Parallel Stencil Convolution in Haskell

• Describes the current array representation.
• Discusses the back-end, how the parallelism works, and how to write fast code.
• Describes Repa's special support for Stencil convolutions.

​Tutorial on usage and optimisation

• Contains lots of simple examples to get you started.
• High level discussion of fusion, optimisation, and how to use the force function.

Examples

Here is the output of some of the examples included in the ​repa-examples package:

fft2d-highpass	Laplace
more info	more info

Demo

There is also an OSX demo that does edge detection on a video stream:

video: ​low(10MB) ​high(40MB)

• The ​source should compile with XCode 3.2.1, GHC 7.0.3 and Repa 2.0.0, but you need to update and run the CONFIGURE.sh script to point it to your GHC install.
• There are also prebuilt OSX i386 versions for ​two ​four and ​six threads. These just have the corresponding +RTS -N# option baked in, you can set it in the main.m module. Some day I will make it so you can select this from the GUI.
• You can also run the edge detector over a single uncompressed .bmp file using the repa-canny program from the ​repa-examples package.